International Consensus Guideline on Delineation of the Clinical Target Volumes at Different Dose Levels for Nasopharyngeal Carcinoma (2024 Version)
Bibliographic record
Abstract
PURPOSE: Radiation therapy planning for nasopharyngeal carcinoma is one of the most challenging tasks for radiation oncologists due to the notoriously narrow therapeutic margin. The first International Guideline (IG-2018 Version) has served as a practical guide for contouring clinical target volumes (CTVs). With increasing data on locoregional extension patterns and outcomes from studies on optimizing CTV and doses, an updated International Guideline is pressingly needed to provide a reference for enhancing precision. METHODS AND MATERIALS: A comprehensive literature review was conducted to assess existing guidelines and emerging data related to contouring. A preliminary questionnaire was distributed to 30 international experts (from 26 centers in 14 countries/regions) with extensive experience in nasopharyngeal carcinoma treatment, aiming to capture diverse practices and opinions. Following initial voting and iterations, a comprehensive survey was prepared for consensus building. RESULTS: The initial questionnaire revealed marked variations in clinical practices related to CTV contouring and prescribed doses among experts. The final Delphi survey consisted of 58 questions: 20 (34%) parameters attained consensus (≥75% agreement) and 32 (55%) attained agreement (60%-74% agreement). In the current guideline (IG-2024), 36 parameters involved changes/clarifications compared with IG-2018. The major differences focus on the use of postinduction chemotherapy gross tumor volume (except in patients with advanced extranodal extension) for CTV(p/n) to 70 Gy equivalent, stepwise refinement of elective coverage to ipsilateral anatomical structures for eccentric primary tumor, selective coverage of nodal levels, and a lower elective dose of 50 Gy equivalent. CONCLUSIONS: Amidst the challenges of diverging practices, a comprehensive consensus guideline has been devised based on updated evidence and collective agreement among international experts. This serves as a practical reference for optimal target coverage at different dose levels to maximize locoregional control while minimizing toxicities and guiding principles for generating automated contouring programs to enhance standardization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".